UTM (UAS Traffic Management) and Spatial Analysis
Research
Regional for urban air mobility application in metropolitan areas: case studies in San Francisco and New York
In this study, as a first step to assess the feasibility of UAM in urban areas, we conduct 3D geodemographic analyses of two major cities in San Francisco, CA and Manhattan, NY. The 3D building footprint data is used to identify the raw available airspace as well as the added spatial restrictions with geofencing. Population data is used to represent the potential customer base by combining the daytime and nighttime population. Since the geospatial and demographic datasets differ in representation, the spatial data is vectorized while population data is available by census tract, spatial information is aggregated in census tracts. In addition, We proposed to group the areas of similar spatial and population characteristics through regionalization. Regionalization is a spatially constrained multi-variate clustering method to group small geographical units (census blocks and tracts in general) into a contiguous region of homogeneous nature. The main benefit of regionalization is to delineate regions of similar characteristics and spatial proximity. Through regionalization, one can better understand the urban space with comprehensive geographic perspective, rather than a small geographical unit of census blocks or tracts. Furthermore, regionalization can also improve geospatial intelligence in urban spaces by delineating the functional neighborhood . In this study, we adopted the SKATER, an efficient regionalization technique that uses minimum spanning tree consisting of a connected tree with no circuits. The intention is to provide a region map of the city that can readily identify regions of similar UAM operational and population characteristics with spatial continuity and feasibility. Based on the regionalization results, correspondence analysis was conducted to translate the compound effect of spatial and population characteristics into feasibility
Density-aware flight planning for multiple agents in urban airspace
The aim of this on-going study is to present a flight planning framework that can be applied in a large-scale map environment, such as city-wide airspace. Two key components of this framework are hierarchical spatial data representation and multi-agent pathfinding algorithm. Choosing an appropriate spatial data structure is crucial for monitoring and planning high-density urban operations in a large-scale map, and we propose octree- and medial axis- based approaches to represent urban airspace. Based on proposed spatial data structures, we adopt and apply a variant of conflict-based search (CBS), proposed by Sharon et al (2015), to assign each agent to a sequence of airspace volumes while taking into account projected traffic density within each volume. Detailed methods and analysis results will be made available in our working paper.
Cooperative sUAV Collision Avoidance
The aim of this study is to present a cooperative collision avoidance approach for sUAV in the low-altitude uncontrolled airspace based on satisficing game theory. By incorporating both the self and cooperative preferences in making individual heading decision, satisficing framework provides a collision avoidance strategy that increases throughput while decreasing unnecessary collisions. Simulation-based sensitivity analysis is conducted with varying satisficing parameters, including number of sUAV agents, minimum separation, action angle, and dual utility parameters of raw preference and negotiation index.
SELECTED REFERENCES
Namwoo Kim, Yoonjin Yoon, "Cooperative sUAV Collision Avoidance Based on Satisficing Theory", International Journal of Aeronautical and Space Sciences, 2019
Geodemographical Risk Analysis of 3D Urban Space
For application of UAS and UAM in urban area, people and man-mad structures that consisting urban environment should be considered. We analyzed urban space by using census tract based population data and 3d building model data. Highly urbanized areas – Manhattan Island and San Francisco were analyzed for their urban space characteristics.
Urban Airspace Availability Assessment
One of the key challenges in enabling large-scale UAS operations at low altitude lies in the limited airspace of densely built-up urban environment. Unlike the high-altitude controlled airspace, the geospatial complexity arising from existing static obstacles such as buildings and terrain poses a new challenge in the UAS traffic flow management. Hence, a more adaptive and intelligent approach is necessary to identify airspace that is not only free of obstacles but also usable or operable within an acceptable level of risk.
As a first step to airspace capacity management, we propose an airspace availability assessment framework that incorporates the underlying geospatial complexity as well as vehicle operational requirements. Specifically, we utilize two types of geofence - keep-out and keep-in.
This interactive tool enables you to explore the airspace availability in three case study areas - Gangnam, Manhattan, and San Francisco. Using this tool, one can identify and compare usable airspace at given altitude with various geofence parameter combinations.
SELECTED REFERENCES
Jungwoo Cho, Yoonjin Yoon, "How to Assess the Capacity of Urban Airspace: A Topological Approach Using Keep-in and Keep-out Geofence", 92, 137-149, Transportation Research, Part C, 2018
Method for identifying available airspace for unmanned aerial vehicle operations, Patent filed, Dec 17, 2017, KR 10-2018-0033978
Horizontal and Vertical Connectivity of Airspace
Low-level airspace contains existing environment of people and surrounding structures that are sensitive to risks posed by UAS operations. In such environment, not all free airspace is available for operational use. In many literatures, however, UTM airspace is often regarded nearly free of obstacles, and geospatial complexity of airspace arising from obstacles is not fully addressed.
In this study, we present topography map and skeletal graph to interpret underlying geometrical and topological properties of urban airspace. Specifically, we present topographic map to provide a quantitative representation of vertical and horizontal dimension of usable airspace. Also, a skeletal graph is extracted to uncover horizontal and vertical connectivity of airspace. Both methods not only provide a compact and informative abstraction of airspace but also can be used to partition the entire airspace into different levels.
SELECTED REFERENCES
Jungwoo Cho, Yoonjin Yoon. "Extraction and Interpretation of Geometrical and Topological Properties of Urban Airspace for UAS Operations". 13th ATM R&D Seminar, 2019
UTM: Urban Drone Routing
- UAV path extraction & visualization in the case study area of Manhattan, NY
- keep-out geofence of 30 meter is applied to mid-town area